Instructions on Handling RAG Classifications with Semi-Structured Data

In a groundbreaking development, a new AI system has been designed to handle the common real-world problem of documents containing both text and tables. The system, which is the brainchild of AI/ML Engineer Harsh Mishra, utilises a unique method that combines intelligent unstructured data parsing with a multi-vector retriever.

The heart of this system is a multi-vector retriever, which links summaries in a vector store to their corresponding raw documents in a docstore. This retriever is capable of finding the summary of Table 1, a discussion on model parameters and training data, within the vast expanse of data.

Once the relevant summaries and raw documents are fetched, they are passed to a language model. This model, aided by the concise summaries generated by a LangChain chain, is then able to generate answers to questions. The system's ability to demonstrate the power of the RAG approach on semi-structured data is evident in its correct answer to a question using the summary and raw data of Table 1.

The method ensures that the complex structure of documents becomes a strength, not a weakness, by providing the language model with complete context in an easy-to-understand manner. This approach allows the system to deliver more robust and accurate results.

For those interested in delving deeper into the workings of this system, the full code is accessible on both the Colab notebook and GitHub repository. The system's design and performance are a testament to the potential of AI in handling and interpreting complex, semi-structured data.

However, it's worth noting that the search results do not provide information about who wrote the guide for creating a RAG pipeline for semi-structured data. Despite this, the system's performance speaks for itself, showcasing the promising future of AI in data processing and analysis.